import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A						=			5_0_0_0_0_0

A					,A						=			os.path.split(__file__)
A						=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 	lowerCamelCase					(	UpperCamelCase     :				datasets.Dataset			,       **UpperCamelCase     :				List[str]							)   ->    Any:
		_lowerCamelCase				   =  dataset.map(**UpperCamelCase							)


@get_duration
def 	lowerCamelCase					(	UpperCamelCase     :				datasets.Dataset			,       **UpperCamelCase     :				List[str]							)   ->    str:
		_lowerCamelCase				   =  dataset.filter(**UpperCamelCase							)


def 	lowerCamelCase					(	)   ->    Dict:
		_lowerCamelCase				   =  {'num examples': SPEED_TEST_N_EXAMPLES}
		with tempfile.TemporaryDirectory() as tmp_dir:
				_lowerCamelCase				   =  datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
				_lowerCamelCase				   =  generate_example_dataset(
				    os.path.join(UpperCamelCase			,       'dataset.arrow'							)			,       UpperCamelCase			,       num_examples=UpperCamelCase							)

				_lowerCamelCase				   =  transformers.AutoTokenizer.from_pretrained('bert-base-cased'			,       use_fast=UpperCamelCase							)

				def tokenize(UpperCamelCase     :				List[Any]							):
						return tokenizer(examples['text']							)

				_lowerCamelCase				   =  map(UpperCamelCase							)

				_lowerCamelCase				   =  map(UpperCamelCase			,       batched=UpperCamelCase							)

				_lowerCamelCase				   =  map(UpperCamelCase			,       function=lambda UpperCamelCase							: None			,       batched=UpperCamelCase							)

				with dataset.formatted_as(type='numpy'							):
						_lowerCamelCase				   =  map(UpperCamelCase			,       function=lambda UpperCamelCase							: None			,       batched=UpperCamelCase							)

				with dataset.formatted_as(type='pandas'							):
						_lowerCamelCase				   =  map(UpperCamelCase			,       function=lambda UpperCamelCase							: None			,       batched=UpperCamelCase							)

				with dataset.formatted_as(type='torch'			,       columns='numbers'							):
						_lowerCamelCase				   =  map(UpperCamelCase			,       function=lambda UpperCamelCase							: None			,       batched=UpperCamelCase							)

				with dataset.formatted_as(type='tensorflow'			,       columns='numbers'							):
						_lowerCamelCase				   =  map(UpperCamelCase			,       function=lambda UpperCamelCase							: None			,       batched=UpperCamelCase							)

				_lowerCamelCase				   =  map(UpperCamelCase			,       function=UpperCamelCase			,       batched=UpperCamelCase							)

				_lowerCamelCase				   =  filter(UpperCamelCase							)

				# Activate later when tokenizer support batched inputs
				# with dataset.formatted_as(type='numpy'):
				#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

		with open(UpperCamelCase			,       'wb'							) as f:
				f.write(json.dumps(UpperCamelCase							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
				benchmark_map_filter()
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))